Comparison Of Measures Used To Assess The Workload Of Monitoring An Unmanned System In A Simulation Mission

Keywords

Monitoring unmanned systems; Physiological measures; Workload

Abstract

As the deployment of unmanned systems becomes increasingly mainstream, it is crucial to understand the effects of the workload (WL) associated with operating and interacting with these systems. There are multiple categories and types of WL measures, but not all meet the criteria for useful measures. It is not uncommon to find that multiple WL measures for the same task do not concur, which raises questions about whether there should be specific WL measures for certain tasks, and if so, how that should be determined. The present experiment investigated the sensitivity of various physiological and self-report measures in detecting changes in WL elicited by different levels of task demands in two tasks. Each participant was asked to assume the role of a Soldier in a human-robot team performing a simulated intelligence, surveillance, and reconnaissance (ISR) mission. The mission entailed performing a change detection task and a peripheral task of maintaining awareness of the robot teammate's location and surroundings. Auditory prompts were presented to probe the participant's situation awareness of the robot, with regard to its direction of travel and features of its surroundings. Physiological devices used to assess WL were the electroencephalogram (EEG), electrocardiogram (ECG), transcranial Doppler (TCD), functional Near-Infrared (fNIR), and eye tracker. Self-report measures included the TLX and DSSQ. Findings from the present experiment inform developers of unmanned systems about the sensitivity of various WL measures in assessing levels of mental demands imposed by working with unmanned systems.

Publication Date

1-1-2015

Publication Title

Procedia Manufacturing

Volume

3

Number of Pages

1006-1013

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.promfg.2015.07.159

Socpus ID

85010006028 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85010006028

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